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hadoop(五) - 分布式计算利器MapReduce加强
一. Partitioner是partitioner的基类，如果需要定制partitioner也需要继承该类。
public class DataCount {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(DataCount.class);
job.setMapperClass(DCMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(DataInfo.class);
job.setReducerClass(DCReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(DataInfo.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setPartitionerClass(DCPartitioner.class);
job.setNumReduceTasks(Integer.parseInt(args[2]));
job.waitForCompletion(true);
}
//Map
public static class DCMapper extends Mapper<LongWritable, Text, Text, DataInfo>{
private Text k = new Text();
@Override
protected void map(LongWritable key, Text value,
Mapper<LongWritable, Text, Text, DataInfo>.Context context)
throws IOException, InterruptedException {
String line = value.toString();
String[] fields = line.split("\t");
String tel = fields[1];
long up = Long.parseLong(fields[8]);
long down = Long.parseLong(fields[9]);
DataInfo dataInfo = new DataInfo(tel,up,down);
k.set(tel);
context.write(k, dataInfo);
}
}
public static class DCReducer extends Reducer<Text, DataInfo, Text, DataInfo>{
@Override
protected void reduce(Text key, Iterable<DataInfo> values,
Reducer<Text, DataInfo, Text, DataInfo>.Context context)
throws IOException, InterruptedException {
long up_sum = 0;
long down_sum = 0;
for(DataInfo d : values){
up_sum += d.getUpPayLoad();
down_sum += d.getDownPayLoad();
}
DataInfo dataInfo = new DataInfo("",up_sum,down_sum);
context.write(key, dataInfo);
}
}
public static class DCPartitioner extends Partitioner<Text, DataInfo>{
private static Map<String,Integer> provider = new HashMap<String,Integer>();
static{
provider.put("138", 1);
provider.put("139", 1);
provider.put("152", 2);
provider.put("153", 2);
provider.put("182", 3);
provider.put("183", 3);
}
@Override
public int getPartition(Text key, DataInfo value, int numPartitions) {
//向数据库或配置信息 读写
String tel_sub = key.toString().substring(0,3);
Integer count = provider.get(tel_sub);
if(count == null){
count = 0;
}
return count;
}
}
}二. 排序和分组
map和reduce阶段进行排序时，比较的是k2。v2是不参与排序比较的。如果要想让v2也进行排序，需要把k2和v2组装成新的类，作为k2，才能参与比较。
public class InfoBean implements WritableComparable<InfoBean>{
private String account;
private double income;
private double expenses;
private double surplus;
public void set(String account,double income,double expenses){
this.account = account;
this.income = income;
this.expenses = expenses;
this.surplus = income - expenses;
}
@Override
public void write(DataOutput out) throws IOException {
out.writeUTF(account);
out.writeDouble(income);
out.writeDouble(expenses);
out.writeDouble(surplus);
}
@Override
public void readFields(DataInput in) throws IOException {
this.account = in.readUTF();
this.income = in.readDouble();
this.expenses = in.readDouble();
this.surplus = in.readDouble();
}
@Override
public int compareTo(InfoBean o) {
if(this.income == o.getIncome()){
return this.expenses > o.getExpenses() ? 1 : -1;
}
return this.income > o.getIncome() ? 1 : -1;
}
@Override
public String toString() {
return income + "\t" + expenses + "\t" + surplus;
}
// get set
}三. Combiners编程
每一个map可能会产生大量的输出，combiner的作用就是在map端对输出先做一次合并，以减少传输到reducer的数据量。combiner最基本是实现本地key的归并，combiner具有类似本地的reduce功能。如果不用combiner，那么所有的结果都是reduce完成，效率会相对低下。使用combiner，先完成的map会在本地聚合，提升速度。
注意：Combiner的输出是Reducer的输入，如果Combiner是可插拔的，添加Combiner绝不能改变最终的计算结果。所以Combiner只应该用于那种Reduce的输入key/value与输出key/value类型完全一致，且不影响最终结果的场景。比如累加，最大值等。
注意：Combiner的输出是Reducer的输入，如果Combiner是可插拔的，添加Combiner绝不能改变最终的计算结果。所以Combiner只应该用于那种Reduce的输入key/value与输出key/value类型完全一致，且不影响最终结果的场景。比如累加，最大值等。
public class InverseIndex {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
//设置jar
job.setJarByClass(InverseIndex.class);
//设置Mapper相关的属性
job.setMapperClass(IndexMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));//words.txt
//设置Reducer相关属性
job.setReducerClass(IndexReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setCombinerClass(IndexCombiner.class);
//提交任务
job.waitForCompletion(true);
}
public static class IndexMapper extends Mapper<LongWritable, Text, Text, Text>{
private Text k = new Text();
private Text v = new Text();
@Override
protected void map(LongWritable key, Text value,
Mapper<LongWritable, Text, Text, Text>.Context context)
throws IOException, InterruptedException {
String line = value.toString();
String[] fields = line.split(" ");
FileSplit inputSplit = (FileSplit) context.getInputSplit();
Path path = inputSplit.getPath();
String name = path.getName();
for(String f : fields){
k.set(f + "->" + name);
v.set("1");
context.write(k, v);
}
}
}
public static class IndexCombiner extends Reducer<Text, Text, Text, Text>{
private Text k = new Text();
private Text v = new Text();
@Override
protected void reduce(Text key, Iterable<Text> values,
Reducer<Text, Text, Text, Text>.Context context)
throws IOException, InterruptedException {
String[] fields = key.toString().split("->");
long sum = 0;
for(Text t : values){
sum += Long.parseLong(t.toString());
}
k.set(fields[0]);
v.set(fields[1] + "->" + sum);
context.write(k, v);
}
}
public static class IndexReducer extends Reducer<Text, Text, Text, Text>{
private Text v = new Text();
@Override
protected void reduce(Text key, Iterable<Text> values,
Reducer<Text, Text, Text, Text>.Context context)
throws IOException, InterruptedException {
String value = http://www.mamicode.com/"";>
四. shuffle每个map有一个环形内存缓冲区，用于存储任务的输出。默认大小100MB（io.sort.mb属性），一旦达到阀值0.8（io.sort.spill.percent）, 一个后台线程把内容写到(spill)磁盘的指定目录（mapred.local.dir）下的新建的一个溢出写文件。
写磁盘前，要partition, sort。如果有combiner，combine排序后数据。等最后记录写完，合并全部溢出写文件为一个分区且排序的文件。
写磁盘前，要partition, sort。如果有combiner，combine排序后数据。等最后记录写完，合并全部溢出写文件为一个分区且排序的文件。
Reducer通过Http方式得到输出文件的分区。TaskTracker为分区文件运行Reduce任务。复制阶段把Map输出复制到Reducer的内存或磁盘。一个Map任务完成，Reduce就开始复制输出。排序阶段合并map输出。然后走Reduce阶段。
hadoop(五) - 分布式计算利器MapReduce加强
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